{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deep-domain-generalization-via-conditional","title":"Deep Domain Generalization via Conditional Invariant Adversarial Networks","arxiv_id":null,"date":"2018-09-01","proceeding":"ECCV 2018 9","authors":["Ya Li","Xinmei Tian","Mingming Gong","Yajing Liu","Tongliang Liu","Kun Zhang","DaCheng Tao"],"abstract":"Domain generalization aims to learn a classification model from multiple source domains and generalize it to unseen target domains. A critical problem in domain generalization involves learning domain-invariant representations. Let $X$ and $Y$ denote the features and the labels, respectively. Under the assumption that the conditional distribution $P(Y|X)$ remains unchanged across domains, earlier approaches to domain generalization learned the invariant representation $T(X)$ by minimizing the discrepancy of the marginal distribution $P(T(X))$. However, such an assumption of stable $P(Y|X)$ does not necessarily hold in practice. In addition, the representation learning function $T(X)$ is usually constrained to a simple linear transformation or shallow networks. To address the above two drawbacks, we propose an end-to-end conditional invariant deep domain generalization approach by leveraging deep neural networks for domain-invariant representation learning. The domain-invariance property is guaranteed through a conditional invariant adversarial network that can learn domain-invariant representations w.r.t. the joint distribution $P(T(X),Y)$ if the target domain data are not severely class unbalanced. We perform various experiments to demonstrate the effectiveness of the proposed method.","url_abs":"http://openaccess.thecvf.com/content_ECCV_2018/html/Ya_Li_Deep_Domain_Generalization_ECCV_2018_paper.html","url_pdf":"http://openaccess.thecvf.com/content_ECCV_2018/papers/Ya_Li_Deep_Domain_Generalization_ECCV_2018_paper.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"domain-generalization","task_name":"Domain Generalization"},{"task_slug":"representation-learning","task_name":"Representation Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/domain-generalization-on-pacs-2","task":"Domain Generalization","dataset":"PACS","model":"CDANN (Resnet-50, DomainBed)","rank_in_archive_order":76,"of":133,"metrics":{"Average Accuracy":"82.6"},"uses_additional_data":false},{"leaderboard":"/sota/domain-generalization-on-pacs-2","task":"Domain Generalization","dataset":"PACS","model":"CIDDG (Alexnet)","rank_in_archive_order":128,"of":133,"metrics":{"Average Accuracy":"68.88"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}